Replicant Thinking Machine

Begin with one bounded workflow that matches Replicant Thinking Machine's core purpose: AI contact-center automation for handling customer conversations and completing routine service workflows. Connect only the knowledge sources, phone numbers, helpdesk data, or business systems required for that workflow, and define what the AI is allowed to answer or do.

Create a small test set of normal questions plus difficult cases such as unclear requests, angry customers, background noise, unsupported topics, and requests involving refunds, account changes, or other consequential actions. Define explicit handoff conditions before enabling autonomous resolution.

Review transcripts, generated answers, tool calls, summaries, classifications, and escalations. Measure resolution quality as well as speed: hallucination rate, transfer accuracy, latency, customer effort, and whether the system preserves the correct customer context. For analytics products, manually audit a sample of classifications and themes.

Before scaling, confirm call-recording and consent obligations, privacy, retention, role permissions, audit logs, rate limits, integration behavior, and expected cost. The catalog currently classifies pricing as contact, but current plans and usage charges should be confirmed with the provider.